Digital-twin-based intelligent control system for machining precision of numerical control machine tool

Abstract

The application discloses a kind of based on digital twinning numerical control machine tool machining precision intelligent control system, belong to numerical control machine tool processing technical field, including data acquisition module, for collecting the data information of numerical control machine tool in machining process, data information includes the motion speed, acceleration and temperature of the spindle of numerical control machine tool, and the cutting force between the tool of numerical control machine tool and workpiece and the wear degree of tool;Data processing module is used for data cleaning and data fusion to data information, and analyzes data information, extracts the feature related to the machining precision of numerical control machine tool to workpiece.The machining precision and production efficiency of numerical control machine tool are improved by digital twinning model and intelligent optimization control, and linear wear compensation and nonlinear wear compensation are introduced, the wear condition of tool can be more accurately evaluated, tool compensation parameters are adjusted in time, so that machining precision is maintained, and the intelligent level of system is enhanced.

Description

Technical Field

[0001] This invention relates to the field of CNC machine tool machining technology, and in particular to an intelligent control system for CNC machine tool machining accuracy based on digital twins. Background Technology

[0002] As the mother machines of industry, the safe, stable operation and high-quality processes of CNC machine tools are crucial for achieving intelligent manufacturing. However, due to inherent mechanical errors, thermal deformation, tool wear, and dynamic changes during machining, it is difficult to guarantee the stability and consistency of machining accuracy. Traditional machining accuracy control methods mainly rely on manual inspection and experience-based adjustments, which are not only inefficient but also fail to achieve high-precision machining.

[0003] Currently, research in this area includes a method for intelligent control of CNC machine tool machining accuracy based on digital twins, as provided in application CN202311515419.6. This technical solution involves establishing a spindle digital twin integrating a digital 3D model, temperature information reshaping, and incremental prediction functions; then, during the warm-up phase, temperature and displacement sensors are deployed, and the spindle temperature information is reshaped using the digital twin to determine synchronous measurement points that meet preset thermal error conditions and predict their temperature changes; the temperature information reshaping and incremental prediction process is repeated. This technical solution provides theoretical guidance for the machine tool warm-up process, enabling control of machining accuracy when sensors cannot be deployed during CNC machine tool machining.

[0004] Another application, CN202211217414.0, provides a digital twin-based adaptive machining method and system for CNC machine tools. This technical solution includes collecting motion and position information of the machine tool and its components; constructing a digital twin virtual scene and modeling a corresponding image model in the virtual digital space; and eliminating idle travel during machining in the digital twin virtual scene using the enclosing structure method and collision detection method. This technical solution can intelligently adjust the spindle feed rate by monitoring changes in the cutting volume of the tool in real time, effectively improving the cutting efficiency of parts machining and protecting the machine tool by reducing downtime.

[0005] However, despite incorporating machine learning and artificial intelligence algorithms, the prediction of machining accuracy in real-world scenarios using digital twins remains limited by data quality and model precision. For example, changes in dynamic factors such as tool wear and thermal deformation are difficult to predict accurately, leading to insufficient precision in optimized control. Summary of the Invention

[0006] In view of the problems existing in the field of CNC machine tool processing technology, the present invention is proposed.

[0007] Therefore, one of the objectives of this invention is to provide an intelligent control system for CNC machine tool machining accuracy based on digital twins. Through digital twin models and intelligent optimization control, it improves the machining accuracy and production efficiency of CNC machine tools. Furthermore, by introducing linear wear compensation and nonlinear wear compensation, it can more accurately assess the wear of the cutting tools and adjust the tool compensation parameters in a timely manner, thereby maintaining machining accuracy and enhancing the intelligence level of the system.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] This invention provides an intelligent control system for machining accuracy of CNC machine tools based on digital twins, comprising:

[0010] The data acquisition module is used to collect data information of the CNC machine tool during the machining process. The data information includes the spindle speed, acceleration and temperature of the CNC machine tool, as well as the cutting force between the CNC machine tool and the workpiece and the degree of tool wear.

[0011] The data processing module is used to clean and fuse the data information, analyze the data information, and extract features related to the machining accuracy of the CNC machine tool on the workpiece.

[0012] The machining accuracy prediction module is used to construct a digital twin model based on the extracted features and collected data information, predict the machining accuracy based on the extracted features in the digital twin model, and evaluate whether the machining accuracy requirements are met based on the prediction results.

[0013] An optimization control module is used to generate optimization control instructions based on the prediction results. The optimization control instructions include adjusting the spindle's motion speed and acceleration, correcting tool compensation parameters, and optimizing the machining path.

[0014] A machining accuracy fusion analysis module is used to analyze the impact of the optimized control command on the machining accuracy of the workpiece; the machining accuracy fusion analysis module includes an analysis unit.

[0015] The analysis unit is used to analyze the regular influence of the optimized control command on the machining accuracy of workpieces of different materials, including workpieces made of hard materials and workpieces made of soft materials.

[0016] In a preferred embodiment of the present invention, the machining accuracy fusion analysis module further includes a calculation unit, a recording unit, and a judgment unit;

[0017] The computing unit responds to the analyzed patterns and influences, and is used to calculate and adjust the optimized control command based on these patterns and influences, categorizing the calculation and adjustment of the optimized control command into... in, This represents the nth calculation adjustment performed on the optimized control command, and calculates the changes in machining accuracy of workpieces of different materials due to the different optimized control commands adjusted.

[0018] The recording unit is used to record the changes, distinguish the changes into changes that improve processing accuracy and changes that decrease processing accuracy, and generate a dataset;

[0019] The determination unit responds to the dataset and is used to determine the optimization control instructions generated for workpiece processing in future time periods based on the optimization control instructions corresponding to the changes in processing accuracy reduction. The optimization control instructions corresponding to the changes in processing accuracy reduction are marked as reference optimization control instructions. If the optimization control instructions generated in future time periods are the same as the reference optimization control instructions, the system determines that it will have an impact on the workpiece processing accuracy reduction; otherwise, it does not make a determination.

[0020] In a preferred embodiment of the present invention, the data processing module analyzes the data information to extract features related to the machining accuracy of the workpiece by the CNC machine tool, and performs the analysis using statistical analysis methods.

[0021] The statistical analysis methods include the analysis of mean, median, mode, standard deviation, and variance, wherein...

[0022] The mean, median, and mode are used to analyze the degree of concentration of data information.

[0023] The standard deviation and variance are used to analyze the dispersion of data information.

[0024] In a preferred embodiment of the present invention, the calculation unit calculates the changes in machining accuracy of workpieces made of different materials due to the different optimized control commands being adjusted, and calculates the results based on the root mean square error and the maximum error, as shown in the following formula:

[0025] Wherein, RMSE represents the root mean square error;

[0026] In the formula, y i This represents the actual measured value of the machining accuracy. This represents the predicted value for machining accuracy, where n represents the number of measurement points used to measure the workpiece.

[0027] ME represents the maximum deviation in machining accuracy.

[0028] In a preferred embodiment of the present invention, tool wear compensation is calculated based on the calculated change in machining accuracy. The tool wear compensation includes linear wear compensation, as shown in the following formula:

[0029] C(t) = C0 + wt;

[0030] In the formula, C(t) represents the compensation value, C0 represents the initial compensation value, w represents the wear rate, and t represents the processing time;

[0031] The linear wear compensation shown is used to adjust the tool compensation value over time to maintain machining accuracy.

[0032] In a preferred embodiment of the present invention, tool wear compensation further includes nonlinear wear compensation, as shown in the following formula:

[0033] C(t) = C0 + w1t + w2t 2 ;

[0034] In the formula, w1 and w2 represent wear coefficients;

[0035] The nonlinear wear compensation shown is used to evaluate tool wear in order to improve machining accuracy.

[0036] In a preferred embodiment of the present invention, thermal deformation compensation for the tool is calculated based on the calculated linear wear compensation and nonlinear wear compensation, wherein the thermal deformation compensation includes linear thermal deformation compensation, as shown in the following formula:

[0037] △L=α△TL0;

[0038] In the formula, ΔL represents the thermal deformation of the tool, α represents the thermal expansion coefficient of the tool, ΔT represents the temperature change of the tool, and L0 represents the initial length of the tool.

[0039] Linear thermal deformation compensation is used to adjust machining parameters based on changes in tool temperature in order to reduce the impact of thermal deformation on the machining accuracy of the workpiece.

[0040] In a preferred embodiment of the present invention, the thermal deformation compensation further includes nonlinear thermal deformation compensation, as shown in the following formula:

[0041] △L=α1△T+α2△T 2 ;

[0042] In the formula, α1 and α2 represent the thermal deformation coefficients;

[0043] The nonlinear thermal deformation compensation is used to handle thermal deformation and improve machining accuracy.

[0044] 1. By constructing a digital twin model, the system can accurately predict machining accuracy and generate optimized control commands based on the prediction results. At the same time, by calculating linear wear compensation and nonlinear wear compensation, it can more accurately assess the wear of the tool and adjust the tool compensation parameters in a timely manner to maintain machining accuracy.

[0045] 2. The system can analyze and optimize the influence of control commands on the machining accuracy of workpieces of different materials (such as hard materials and soft materials), thereby providing customized machining parameter optimization schemes for workpieces of different materials and improving the versatility and adaptability of the system.

[0046] 3. The system can dynamically adjust and optimize control commands based on real-time collected data, quickly adapting to various changes during the machining process, such as tool wear and thermal deformation, to ensure the stability of machining accuracy. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0048] Figure 1 This is a schematic diagram of the modular structure of the intelligent control system for machining accuracy of CNC machine tools based on digital twins, according to an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of the process structure of an embodiment of the present invention;

[0050] The numbers in the diagram are: 110 - Data acquisition module; 120 - Data processing module; 130 - Machining accuracy prediction module; 140 - Optimization control module; 150 - Machining accuracy fusion analysis module; 1501 - Analysis unit; 1502 - Calculation unit; 1503 - Recording unit; 1504 - Judgment unit. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0052] While existing digital twin technologies incorporate machine learning and artificial intelligence algorithms, the prediction of machining accuracy in real-world scenarios remains limited by data quality and model precision. For example, changes in dynamic factors such as tool wear and thermal deformation are difficult to predict accurately, leading to insufficient precision in optimized control.

[0053] Based on this, the present invention proposes an intelligent control system for CNC machine tool machining accuracy based on digital twins. Through digital twin models and intelligent optimization control, it improves the machining accuracy and production efficiency of CNC machine tools. Furthermore, by introducing linear wear compensation and nonlinear wear compensation, it can more accurately assess the wear of the tool and adjust the tool compensation parameters in a timely manner, thereby maintaining machining accuracy and enhancing the intelligence level of the system.

[0054] The present solution will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0055] Reference Figures 1 to 2 As one embodiment of the present invention, this embodiment provides an intelligent control system for CNC machine tool machining accuracy based on digital twins, comprising:

[0056] The data acquisition module 110 is used to collect data information of the CNC machine tool during the machining process. The data information includes the spindle speed, acceleration and temperature of the CNC machine tool, as well as the cutting force between the CNC machine tool and the workpiece and the degree of tool wear.

[0057] In this embodiment, speed sensors, acceleration sensors, temperature sensors, force sensors, and tool wear sensors are installed inside the CNC machine tool to collect data information during the machining process;

[0058] The data processing module 120 is used to clean and fuse data information, analyze data information, and extract features related to the machining accuracy of CNC machine tools on workpieces.

[0059] In this embodiment, noise and redundant data can be effectively removed, data quality can be improved, and accurate input can be provided for subsequent processing accuracy prediction and optimization.

[0060] The machining accuracy prediction module 130 is used to construct a digital twin model based on the extracted features and collected data information, predict the machining accuracy based on the extracted features in the digital twin model, and evaluate whether the machining accuracy requirements are met based on the prediction results.

[0061] In this embodiment, the machining accuracy prediction module uses a digital twin model to predict the machining accuracy and assess whether it meets the machining accuracy requirements. This prediction method based on digital twins can detect potential machining accuracy problems in advance and provide a scientific basis for optimizing control.

[0062] The optimization control module 140 is used to generate optimization control instructions based on the prediction results. The optimization control instructions include adjusting the spindle's motion speed and acceleration, correcting tool compensation parameters, and optimizing the machining path.

[0063] In this embodiment, this real-time optimization can dynamically adjust the processing procedure to ensure the stability and consistency of processing accuracy;

[0064] The machining accuracy fusion analysis module 150 is used to analyze the impact of optimization control commands on the machining accuracy of the workpiece; the machining accuracy fusion analysis module 150 includes an analysis unit 1501.

[0065] Analysis unit 1501 is used to analyze the regular influence of optimization control commands on the machining accuracy of workpieces of different materials, including workpieces made of hard materials and workpieces made of soft materials.

[0066] In this embodiment, the system can provide customized optimization solutions for workpieces of different materials, thereby improving the system's versatility and adaptability.

[0067] Simultaneously, it can analyze and optimize the influence of control commands on the machining accuracy of workpieces made of different materials. This analysis helps to deeply understand the relationship between machining parameters and machining accuracy, providing support for optimized control.

[0068] The machining accuracy fusion analysis module 150 also includes a calculation unit 1502, a recording unit 1503, and a judgment unit 1504;

[0069] The calculation unit 1502 responds to the analyzed patterns and influences, and uses this information to calculate and adjust the optimized control commands based on these patterns and influences. The calculation and adjustment of the optimized control commands are categorized into... in, This represents the nth calculation adjustment to the optimized control command, and calculates the changes in machining accuracy of workpieces of different materials due to the different optimized control commands adjusted.

[0070] In this embodiment, such precise adjustment can ensure the effectiveness and relevance of optimized control commands, thereby improving machining accuracy;

[0071] The recording unit 1503 is used to record changes, distinguish between changes that improve machining accuracy and changes that decrease machining accuracy, and generate a dataset;

[0072] In this embodiment, the dataset provides rich historical data for subsequent optimization control, which facilitates further analysis and improvement;

[0073] The decision unit 1504 responds to the dataset and is used to determine the optimized control instructions generated for workpiece processing in the future time period based on the optimized control instructions corresponding to the change in processing accuracy reduction. The optimized control instructions corresponding to the change in processing accuracy reduction are marked as reference optimized control instructions. If the optimized control instructions generated in the future time period are the same as the reference optimized control instructions, the system determines that it will cause a change in processing accuracy reduction to the workpiece; otherwise, it does not make a determination.

[0074] In this embodiment, the determination unit makes a determination on the optimization control instructions for future time periods based on the dataset, promptly detects instructions that may lead to a decrease in processing accuracy, and issues an early warning. This intelligent determination mechanism can effectively avoid a decrease in processing accuracy and improve the reliability of the system.

[0075] In the data processing module, the data information is analyzed to extract features related to the machining accuracy of the CNC machine tool on the workpiece, and the analysis is performed through statistical analysis methods.

[0076] Statistical analysis methods include the analysis of mean, median, mode, standard deviation, and variance.

[0077] Mean, median, and mode are used to analyze the degree of centrality of data information;

[0078] Standard deviation and variance are used to analyze the dispersion of data information;

[0079] In this embodiment, in one feasible implementation, by calculating the mean and standard deviation of the cutting force data, the average level and fluctuation of the cutting force can be understood, thereby determining the stability of the machining process;

[0080] By analyzing the mean, median, and mode, we can understand the central tendency of the data and provide a reference for the stability of the processing.

[0081] By analyzing standard deviation and variance, we can understand the dispersion of data and assess the fluctuations in the processing. This analysis helps to detect abnormal fluctuations in a timely manner and provides a basis for optimizing control.

[0082] In the calculation unit, the changes in machining accuracy of workpieces made of different materials are calculated based on the root mean square error and the maximum error, as shown in the following formula:

[0083] Wherein, RMSE represents the root mean square error;

[0084] In the formula, y i This represents the actual measured value of the machining accuracy. This represents the predicted value for machining accuracy, where n represents the number of measurement points used to measure the workpiece.

[0085] Root Mean Square Error (RMSE);

[0086] RMSE is used to evaluate machining accuracy; the smaller the value, the higher the accuracy.

[0087] Where ME represents the maximum deviation in machining accuracy;

[0088] Maximum Error (ME), the smaller the value, the higher the precision;

[0089] By calculating the root mean square error (RMSE), the difference between the actual measured value and the predicted value of machining accuracy can be quantified, providing an intuitive indicator for the evaluation of machining accuracy.

[0090] By calculating the maximum error (ME), the maximum deviation that may occur during the machining process can be identified, providing a conservative reference value for the evaluation of machining accuracy. This calculation helps to ensure that the machining accuracy is within the allowable range.

[0091] Based on the calculated change in machining accuracy, the tool wear compensation for this change is calculated. Tool wear compensation includes linear wear compensation, as shown in the following formula:

[0092] C(t) = C0 + wt;

[0093] In the formula, C(t) represents the compensation value, C0 represents the initial compensation value, w represents the wear rate, and t represents the processing time;

[0094] The linear wear compensation shown is used to adjust the tool compensation value according to time to maintain machining accuracy;

[0095] In this embodiment, linear wear compensation adjusts the tool compensation value according to the machining time, which can effectively address the impact of tool wear on machining accuracy.

[0096] By dynamically adjusting the tool compensation value, the stability of machining accuracy can be maintained and dimensional deviations caused by tool wear can be reduced.

[0097] Tool wear compensation also includes nonlinear wear compensation, as shown in the following formula:

[0098] C(t) = C0 + w1t + w2t 2 ;

[0099] In the formula, w1 and w2 represent wear coefficients;

[0100] The nonlinear wear compensation shown is used to evaluate tool wear in order to improve machining accuracy;

[0101] In this embodiment, nonlinear wear compensation can more accurately assess the wear condition of the tool and is suitable for machining scenarios with complex wear patterns. This compensation method can improve machining accuracy, especially when the tool wear is severe.

[0102] This more accurate wear assessment and compensation can further improve machining accuracy and ensure machining quality;

[0103] Thermal deformation compensation for the tool is calculated based on the calculated linear and nonlinear wear compensation. Thermal deformation compensation includes linear thermal deformation compensation, as shown in the following formula:

[0104] △L=α△TL0;

[0105] In the formula, ΔL represents the thermal deformation of the tool, α represents the thermal expansion coefficient of the tool, ΔT represents the temperature change of the tool, and L0 represents the initial length of the tool.

[0106] Linear thermal deformation compensation is used to adjust machining parameters according to the temperature change of the tool, so as to reduce the impact of thermal deformation on the machining accuracy of the workpiece;

[0107] In this embodiment, by dynamically adjusting the processing parameters, the processing accuracy can be optimized and the dimensional deviation caused by thermal deformation can be reduced.

[0108] Thermal deformation compensation also includes nonlinear thermal deformation compensation, as shown in the following formula:

[0109] △L=α1△T+α2△T 2 ;

[0110] In the formula, α1 and α2 represent the thermal deformation coefficients;

[0111] Nonlinear thermal deformation compensation is used to handle thermal deformation and improve machining accuracy;

[0112] In this embodiment, nonlinear thermal deformation compensation can handle thermal deformation more accurately and is suitable for processing scenarios with complex thermal deformation patterns. This compensation method can further improve processing accuracy, especially when thermal deformation is severe.

[0113] More accurate thermal deformation compensation can further optimize machining precision and ensure machining quality.

[0114] In summary, this application achieves a significant improvement in the machining accuracy of CNC machine tools through digital twin models and intelligent optimization control. The system not only comprehensively collects and processes data during the machining process but also dynamically adjusts the machining process through precise prediction and optimization control commands, ensuring the stability and consistency of machining accuracy. Furthermore, the system considers the machining characteristics of workpieces made of different materials, providing customized optimization solutions and improving the system's versatility and adaptability. Through linear and nonlinear wear compensation and thermal deformation compensation, the system effectively addresses the impact of tool wear and thermal deformation on machining accuracy, further improving machining accuracy and production efficiency.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A digital-twin-based intelligent control system for machining precision of a numerical control machine tool, characterized in that, The application relates to a machining precision prediction and optimization system for a numerical control machine tool, comprising: a data acquisition module for acquiring data information of the numerical control machine tool in a machining process, wherein the data information comprises the motion speed and acceleration of a spindle of the numerical control machine tool, the temperature of the numerical control machine tool, the cutting force between a tool and a workpiece of the numerical control machine tool, and the wear degree of the tool; a data processing module for data cleaning and data fusion of the data information and analysis of the data information to extract features related to machining precision of the numerical control machine tool on the workpiece; a machining precision prediction module for constructing a digital twin model according to the extracted features and the acquired data information, predicting machining precision in the digital twin model according to the extracted features, and evaluating whether the machining precision meets the requirements based on the predicted results; an optimization control module for generating optimization control instructions according to the predicted results, wherein the optimization control instructions comprise adjustment of the motion speed and acceleration of the spindle, correction of tool compensation parameters, and optimization of a machining path; a machining precision fusion analysis module for analyzing the influence of the optimization control instructions on the machining precision of the workpiece, wherein the machining precision fusion analysis module comprises an analysis unit; the analysis unit is used for analyzing the regular influence of the optimization control instructions on the machining precision of workpieces of different materials, wherein the workpieces of different materials comprise workpieces of hard materials and workpieces of soft materials; the machining precision fusion analysis module further comprises a calculation unit, a recording unit and a judgment unit; The computing unit is used for computing adjustment of the optimization control instruction according to the analyzed regular influence, and the computing adjustment of the optimization control instruction is divided into △1, △2,..., △n. n Wherein, △1, △2,..., △n represent the n-th computing adjustment of the optimization control instruction, and the change of the machining precision of the workpiece of different materials by the adjusted different optimization control instructions is calculated. n ​ the recording unit is used for recording the changes and distinguishing the changes into changes of improved machining precision and changes of reduced machining precision, and generating a data set; the judgment unit is used for judging the optimization control instructions generated in a future period for machining the workpiece based on the optimization control instructions corresponding to the changes of reduced machining precision, and marking the optimization control instructions corresponding to the changes of reduced machining precision as reference optimization control instructions, wherein if the optimization control instructions generated in the future period are the same as the reference optimization control instructions, the system judges that the optimization control instructions will cause the influence of the changes of reduced machining precision on the workpiece; otherwise, the system does not judge.

2. The digital-twin-based intelligent control system for machining precision of a CNC machine tool according to claim 1, characterized in that, in the data processing module, the features related to the machining precision of the numerical control machine tool on the workpiece are extracted by analyzing the data information through a statistical analysis method; the statistical analysis method comprises analysis of mean value, median, mode, standard deviation and variance, wherein the mean value, the median and the mode are used for analyzing the concentration degree of the data information; the standard deviation and the variance are used for analyzing the dispersion degree of the data information.

3. The digital-twin-based intelligent control system for machining precision of a CNC machine tool according to claim 1, characterized in that, in the calculation unit, the changes of machining precision of workpieces of different materials caused by the adjusted different optimization control instructions are calculated according to the root mean square error and the maximum error, and the formula is as follows: where RMSE denotes the root mean square error; In the formula, y i represents the actual measured value of the machining precision, represents the predicted value of the machining precision, and n represents the number of measurement points at which the workpiece is measured. where ME is the maximum deviation of the machining precision.

4. The digital-twin-based intelligent control system for machining precision of a CNC machine tool according to claim 3, characterized in that, based on the calculated changes of machining precision, the tool wear compensation in the changes is calculated, the tool wear compensation comprises linear wear compensation, and the formula is as follows: C(t) = Co +wt ; in the formula, C(t) represents a compensation value, C0 represents an initial compensation value, w represents a wear rate, and t represents machining time; the linear wear compensation is used for adjusting the tool compensation value according to time to maintain the machining precision.

5. The digital-twin-based intelligent control system for machining precision of a CNC machine tool according to claim 4, characterized in that, The tool wear compensation also includes nonlinear wear compensation, and a formula is as follows: C(t) = Co + wlt + w2t 2 ; In the formula, w1 and w2 represent wear coefficients; The nonlinear wear compensation is used to evaluate the wear condition of the tool, so as to improve the machining precision.

6. The digital-twin-based intelligent control system for machining precision of a numerical control machine tool according to any one of claims 4-5, characterized in that, According to the calculated linear wear compensation and nonlinear wear compensation, a thermal deformation compensation of the tool is calculated, the thermal deformation compensation includes linear thermal deformation compensation, and a formula is as follows: ΔL = αΔTL0; In the formula, ΔL represents the thermal deformation amount of the tool, α represents the thermal expansion coefficient of the tool, ΔT represents the temperature change of the tool, and L0 represents the initial length of the tool; The linear thermal deformation compensation is used to adjust the machining parameters according to the temperature change of the tool, so as to reduce the influence of the thermal deformation on the machining precision of the workpiece.

7. The digital-twin-based intelligent control system for machining precision of a CNC machine tool according to claim 6, characterized in that, The thermal deformation compensation also includes nonlinear thermal deformation compensation, and a formula is as follows: AL = a1AT + a2AT 2 ; In the formula, α1 and α2 represent thermal deformation coefficients; The nonlinear thermal deformation compensation is used to process the thermal deformation condition, so as to improve the machining precision.

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